SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 4426–4450 of 6661 papers

TitleStatusHype
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Learning Dense Correspondences between Photos and Sketches—0
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech DetectionCode0
Phase Matching for Out-of-Distribution Generalization—0
Towards a Visual-Language Foundation Model for Computational Pathology—0
General-Purpose Multi-Modal OOD Detection Framework—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalCode0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Learning Discriminative Visual-Text Representation for Polyp Re-IdentificationCode0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
Towards the Sparseness of Projection Head in Self-Supervised Learning—0
Contrastive Multi-Task Dense Prediction—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Semantic Contrastive Bootstrapping for Single-positive Multi-label RecognitionCode0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified